<![CDATA[Resource efficiency lies at the heart of logistics performance, with unloading operations in storage facilities serving as a critical determinant of overall productivity. In less developed regions, the widespread reliance on basic rules-based systems such as FIFO often proves inadequate for handling operational complexities, leading to bottlenecks and inefficiencies. Small and medium-sized enterprises (SMEs), constrained by limited resources, are compelled to optimize existing infrastructure rather than invest in costly upgrades. To address this challenge, the present study introduces a goal-oriented programming model designed to assign trucks to loading docks within specific time slots, thereby enhancing time efficiency. The model evaluates performance across four key metrics: waiting time, loading time, overtime, and equity. By leveraging goal programming, numerical prioritization of these objectives becomes possible, enabling flexible adjustments to meet operational needs. Furthermore, Monte Carlo simulation (MCS) is employed to incorporate variability into the dataset and assess model robustness under real-world uncertainty. Experimental results reveal that the proposed approach consistently outperforms traditional systems, delivering significant improvements in time efficiency. These findings highlight the potential of goal programming as a practical solution for planning in resource-constrained environments. The resulting model offers an adaptive, reliable framework that warehouse managers can implement without incurring substantial infrastructure costs.]]>
